FluiqFluiqDocs
  • ObservabilityTrace every call, cost, and latency
  • SecurityBlock attacks, redact PII and secrets
  • OptimizationCache repeated prompts automatically
  • EvaluationScore responses and whole agent runs
  • DatasetsGolden sets that capture whole agent runs
  • Prompt ManagementVersion and deploy prompt templates
  • AlertsPush eval and security events to Slack

LLM Providers

  • OpenAI
  • Anthropic
  • Google Gemini
  • Google Vertex AI

Agent Frameworks

  • LangChain
  • LangGraph
  • CrewAI
  • Google ADK
  • MCP

Vector Databases

  • Pinecone
  • Chroma
  • Weaviate
  • FAISS
  • Qdrant
14 integrations · zero wrappersView all
Pricing
  • Fluiq DocsGuides, concepts & SDK reference
  • Code SamplesCopy-paste integration snippets
  • LLM Cost CalculatorCompare OpenAI, Claude & Gemini pricing
  • polygateOpen-source unified LLM client
  • InfragerCloud diagrams to secure Terraform
Contact
Get API key

Language

Getting Started

Quickstart

Pillars

ObservabilitySecurityEvaluationOptimization

Reference

PromptsDatasetsConfigurationAlerts
Code Examples
SDK reference

Fluiq Python SDK

Two lines of Python instrument any AI agent or LLM pipeline. Auto-traced integrations for OpenAI, Anthropic, Gemini, LangChain, and MCP, plus a @trace decorator for everything else. Server-side security scanning, Redis caching, and LLM-as-judge evaluations are one method call each, all on Fluiq infrastructure, nothing to deploy.

Quickstart

Install the package, grab an API key, and call instrument() once at startup. Every supported LLM call from that point is traced automatically.

1

Install

Python
pip install fluiq
2

Get an API key

Create a free account and copy your key from the dashboard. Sign up →

3

Instrument once at startup

Python
import fluiq

fluiq.instrument(api_key="fl_...") # or set FLUIQ_API_KEY to environment

# Every OpenAI / Anthropic / Gemini / LangChain / MCP call from this
# point on is traced automatically, free and unlimited on every tier.
# Opt in to more when you want it:
fluiq.eval(thresholds={"hallucination": 0.8})  # LLM-as-judge scoring
fluiq.optimize()   # Redis caching (Team+)
fluiq.secure()     # Security scanning (Growth+)

Next steps

Open the dashboard
Watch traces stream in, inspect costs by node, and review quality scores.
Go to dashboard
Add the eval gate
Gate every PR on hallucination, faithfulness, and relevancy thresholds.
See eval gate setup
Enable optimization
One method call activates Redis caching driven by your trace history.
Upgrade to Team
Manage prompts
Discover, edit, evaluate, and promote prompt templates to dev, staging, and production.
Open Prompts